Wszystkie pola: Grzesiak, Łukasz - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Implantation of superabsorbent polymer by magnetic iron oxide particles
Autorzy:: Grzesiak, Maciej
Szajnecki, Łukasz
Powiązania:: https://bibliotekanauki.pl/articles/2079886.pdf
Data publikacji:: 2019
Wydawca:: Uniwersytet Marii Curie-Skłodowskiej. Wydawnictwo Uniwersytetu Marii Curie-Skłodowskiej
Tematy:: superabsorbents
hydrogels
magnetic particles
swelling properties
Opis:: The aim of this research was to optimise methods of magnetic particles of iron oxides implantation into superabsorbent polymer based on partially neutralized acrylic acid (SAc/AAc) crosslinked with ethylene glycol dimethacrylate (EGDMA). The structure of the obtained materials was confirmed by infrared spectroscopic analysis (FT-IR/ATR). The quality of implantation process was evaluated on the basis of microscopy images. Swelling properties of the obtained implanted materials were also determined.
Źródło:: Annales Universitatis Mariae Curie-Skłodowska, sectio AA – Chemia; 2019, 74, 2; 101-113
2083-358X
Pojawia się w:: Annales Universitatis Mariae Curie-Skłodowska, sectio AA – Chemia
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Artificial Neural Network-Based Gain-Scheduled State Feedback Speed Controller for Synchronous Reluctance Motor
Autorzy:: Tarczewski, Tomasz
Niewiara, Łukasz J.
Grzesiak, Lech M.
Powiązania:: https://bibliotekanauki.pl/articles/1956002.pdf
Data publikacji:: 2021
Wydawca:: Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:: synchronous reluctance motor
state feedback controller
gain scheduling
artificial neural network
robustness analysis
Opis:: This paper focuses on designing a gain-scheduled (G-S) state feedback controller (SFC) for synchronous reluctance motor (SynRM) speed control with non-linear inductance characteristics. The augmented model of the drive with additional state variables is introduced to assure precise control of selected state variables (i.e. angular speed and d-axis current). Optimal, non-constant coefficients of the controller are calculated using a linear-quadratic optimisation method. Non-constant coefficients are approximated using an artificial neural network (ANN) to assure superior accuracy and relatively low usage of resources during implementation. To the best of our knowledge, this is the first time when ANN-based gain-scheduled state feedback controller (G-S SFC) is applied for speed control of SynRM. Based on numerous simulation tests, including a comparison with a signum-based SFC, it is shown that the proposed solution assures good dynamical behaviour of SynRM drive and robustness against q-axis inductance, the moment of inertia and viscous friction fluctuations.
Źródło:: Power Electronics and Drives; 2021, 6, 41; 276-288
2451-0262
2543-4292
Pojawia się w:: Power Electronics and Drives
Dostawca treści:: Biblioteka Nauki

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